linkage between data resources and analytical workflows
1. Robust methods for multi-table data
Community-driven open-source project
High-quality, tested statistical methods
Well-documented
2. Standardized, user-friendly approaches
Methods for microbiome data science
Online book
Graphical user interface
3. Linkage between data resources and analytical workflows
Demonstration
# Get data from HoloFood databasemae <- HoloFoodR::getResult(ids)
# Get data from HoloFood databasemae <- HoloFoodR::getResult(ids)# Get data from MGnify databasetse <- MGnifyR::getResult(ids)
# Get data from HoloFood databasemae <- HoloFoodR::getResult(ids)# Get data from MGnify databasetse <- MGnifyR::getResult(ids)# Merge datasetsmae <-addMGnify(tse, mae)
# Get data from HoloFood databasemae <- HoloFoodR::getResult(ids)# Get data from MGnify databasetse <- MGnifyR::getResult(ids)# Merge datasetsmae <-addMGnify(tse, mae)print(mae)
A MultiAssayExperiment object of 2 listed
experiments with user-defined names and respective classes.
Containing an ExperimentList class object of length 2:
[1] microbiota: TreeSummarizedExperiment with 262 rows and 40 columns
[2] metabolites: TreeSummarizedExperiment with 38 rows and 40 columns
Functionality:
experiments() - obtain the ExperimentList instance
colData() - the primary/phenotype DataFrame
sampleMap() - the sample coordination DataFrame
`$`, `[`, `[[` - extract colData columns, subset, or experiment
*Format() - convert into a long or wide DataFrame
assays() - convert ExperimentList to a SimpleList of matrices
exportClass() - save data to flat files